Electric Polarization from Many-Body Neural Network Ansatz

TL;DR

This paper introduces a neural network wavefunction approach combined with quantum Monte Carlo to accurately compute dielectric responses and polarization in various systems, outperforming traditional methods and aligning with experimental data.

Contribution

The authors develop a neural network ansatz with quantum Monte Carlo to incorporate electron correlations into polarization calculations, enhancing accuracy over conventional density functional theory.

Findings

Outperforms density functional theory in polarization calculations

Accurately reproduces experimental dielectric data

Re-establishes thickness dependence of bilayer graphene's dielectric constant

Abstract

Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wavefunction ansatz combined with quantum Monte Carlo to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data. Furthermore, we have studied the out-of-plane dielectric constant of bilayer graphene using our method and re-established its thickness dependence. Overall, this approach provides a powerful tool to consider electron correlation in the modern theory of polarization.